Financial Crimes: Psychological, Technological, and Ethical Issues

Dion, Michel; Weisstub, David N.; Richet, Jean-Loup

doi:10.1007/978-3-319-32419-7

Cited by 2 publications

References 191 publications

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Financial C.R.I.M.Es in small businesses: causes and consequences

Othman

Laswad

Berkahn

2020

JFC

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Purpose The purpose of this paper is to examine the causes and consequences of financial crimes perpetrated against New Zealand small businesses. Design/methodology/approach A random sample of 200 court cases was selected from 2010 to 2017. A total of 12 cases involving 14 small businesses were analysed. Findings The results reveal that financial crime is a systemic problem and involves people with diverse demographics, and the victims are not restricted to any specific type of small business. The offenders are mostly middle-level managers. The length of offence varied from 1 year to 12.5 years. Most of them funnelled the stolen money into their personal accounts. The common motive is “keeping up appearance”. The management placed immense trust in their employees and did not vet candidates before employment. The losses suffered by small entities ranged from $6,000 to $590,000 and liquidated one business. The severity of the actual court cases indicates the necessity of an employee screening as the first line of defence in these businesses. Research limitations/implications The small sample of court cases is a limitation, but the study contributes to the fraud auditing literature by examining actual court cases involving small businesses. Small businesses as victims of employee fraud and their lack of internal controls are known but under-researched to promote thought about fraud risk severity in these businesses. Originality/value The C.R.I.M.E model has yet been tested on fraud cases involving small businesses.

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Financial C.R.I.M.Es in small businesses: causes and consequences

Othman

Laswad

Berkahn

2020

JFC

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Optical font recognition in smartphone-captured images and its applicability for ID forgery detection

Chernyshova

Aliev

Gushchanskaia

et al. 2019

Eleventh International Conference on Machine Vision (ICMV 2018)

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In this paper, we consider the problem of detecting counterfeit identity documents in images captured with smartphones. As the number of documents contain special fonts, we study the applicability of convolutional neural networks (CNNs) for detection of the conformance of the fonts used with the ones, corresponding to the government standards. Here, we use multi-task learning to differentiate samples by both fonts and characters and compare the resulting classifier with its analogue trained for binary font classification. We train neural networks for authenticity estimation of the fonts used in machine-readable zones and ID numbers of the Russian national passport and test them on samples of individual characters acquired from 3238 images of the Russian national passport. Our results show that the usage of multi-task learning increases sensitivity and specificity of the classifier. Moreover, the resulting CNNs demonstrate high generalization ability as they correctly classify fonts which were not present in the training set. We conclude that the proposed method is sufficient for authentication of the fonts and can be used as a part of the forgery detection system for images acquired with a smartphone camera.

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Financial Crimes: Psychological, Technological, and Ethical Issues

Cited by 2 publications

References 191 publications

Financial C.R.I.M.Es in small businesses: causes and consequences

Financial C.R.I.M.Es in small businesses: causes and consequences

Optical font recognition in smartphone-captured images and its applicability for ID forgery detection

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